Merge pull request #953 from JuliaAI/dev

For a 1.1.0 release

Author: ablaom

.github/codecov.yml

@@ -0,0 +1,5 @@
+coverage:
+  status:
+    project:
+      default:
+        threshold: 0.5%

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJBase"
 uuid = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>"]
-version = "1.0.1"
+version = "1.1.0"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -22,6 +22,7 @@ Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
 PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 ScientificTypes = "321657f4-b219-11e9-178b-2701a2544e81"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
@@ -52,11 +53,13 @@ OrderedCollections = "1.1"
 Parameters = "0.12"
 PrettyTables = "1, 2"
 ProgressMeter = "1.7.1"
+RecipesBase = "1"
 Reexport = "1.2"
 ScientificTypes = "3"
 StatisticalMeasures = "0.1.1"
 StatisticalMeasuresBase = "0.1.1"
 StatisticalTraits = "3.2"
+Statistics = "1"
 StatsBase = "0.32, 0.33, 0.34"
 Tables = "0.2, 1.0"
 julia = "1.6"

README.md

@@ -1,4 +1,4 @@
-## MLJBase  
+## MLJBase
 
 Repository for developers that provides core functionality for the
 [MLJ](https://github.com/alan-turing-institute/MLJ.jl) machine

docs/make.jl

@@ -8,7 +8,6 @@ makedocs(;
     format=Documenter.HTML(prettyurls = get(ENV, "CI", nothing) == "true"),
     pages=[
         "Home" => "index.md",
-        "Measures" => "measures.md",
         "Resampling" => "resampling.md",
         "Composition" => "composition.md",
         "Datasets" => "datasets.md",

docs/src/measures.md

@@ -91,6 +91,9 @@ const Dist = Distributions
 # Measures
 import StatisticalMeasuresBase
 
+# Plots
+using RecipesBase: RecipesBase, @recipe
+
 # from Standard Library:
 using Statistics, LinearAlgebra, Random, InteractiveUtils
 
@@ -172,6 +175,8 @@ include("data/datasets_synthetic.jl")
 
 include("default_measures.jl")
 
+include("plots.jl")
+
 include("composition/models/stacking.jl")
 
 const EXTENDED_ABSTRACT_MODEL_TYPES = vcat(

src/MLJBase.jl

@@ -218,8 +218,9 @@ function check(model::Model, scitype_check_level, args...)
         return is_okay
     end
 
-    # we use `elscitype` here instead of `scitype` because the data is
-    # wrapped in source nodes:
+    # Sometimes (X, ) is a table, when X is a table, which leads to scitype((X,)) =
+    # Table(...) where `Tuple{scitype(X)}` is wanted.  Also, we use `elscitype` here
+    # instead of `scitype` because the data is wrapped in source nodes;
     S = Tuple{elscitype.(args)...}
     if !(S <: F)
         is_okay = false

src/machines.jl

@@ -0,0 +1,5 @@
+@recipe function default_machine_plot(mach::Machine)
+    # Allow downstream packages to define plotting recipes
+    # for their own machine types.
+    mach.fitresult
+end